/*******************************************************************************
 * Copyright (C) 2017 Chang Wei Tan
 * 
 * This file is part of FastWWSearch.
 * 
 * FastWWSearch is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, version 3 of the License.
 * 
 * FastWWSearch is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with FastWWSearch.  If not, see <http://www.gnu.org/licenses/>.
 ******************************************************************************/
package tsml.classifiers.legacy.elastic_ensemble.fast_window_search.experiments;

import java.io.File;
import java.io.FileFilter;
import java.io.FileWriter;
import java.io.IOException;
import java.util.Arrays;

import tsml.classifiers.legacy.elastic_ensemble.fast_window_search.items.ExperimentsLauncher;
import tsml.classifiers.legacy.elastic_ensemble.fast_window_search.tools.Sampling;
import tsml.classifiers.legacy.elastic_ensemble.fast_window_search.tools.UCRArchive;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import tsml.classifiers.legacy.elastic_ensemble.fast_window_search.windowSearcher.UCRSuite;

/**
 * Code for the paper "Efficient search of the best warping window for Dynamic Time Warping" published in SDM18
 * 
 * Experiment to search for the best warping window using KDD12 (UCR Suite) method 
 * but using Lazy UCR Suite instead of the original algorithm 
 * 
 * KDD12 paper:
 * Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., ... & Keogh, E. (2012, August). 
 * Searching and mining trillions of time series subsequences under dynamic time warping. 
 * In Proceedings of the 18th ACM SIGKDD international conference 
 * on Knowledge discovery and data mining (pp. 262-270). ACM. Chicago
 * 
 * @author Chang Wei Tan
 *
 */
public class UCR_UCRSuite {
	private static String osName, datasetName, username, projectPath, datasetPath, resDir, sampleType, method;
	private static int bestWarpingWindow;
	private static double bestScore;
	private static int nbRuns = 1;
	
	public static void main(String[] args) throws Exception {
		// Initialise
		sampleType = "Single";			// Doing just 1 dataset, can be Sorted, Small, New or All
		datasetName = "ArrowHead";		// Name of dataset to be tested
		method = "UCRSuite";					// Method type in finding the best window

		// Get project and dataset path
		osName = System.getProperty("os.name");		
		username = System.getProperty("user.name");
		if (osName.contains("Window")) {
			projectPath = "C:/Users/" + username + "/workspace/SDM18/";
			if (sampleType.equals("New")) 
				datasetPath = "C:/Users/" + username + "/workspace/Dataset/TSC_Problems/";
			else
				datasetPath = "C:/Users/" + username + "/workspace/Dataset/UCR_Time_Series_Archive/";
		} else {
			projectPath = "/home/" + username + "/workspace/SDM18/";
			if (sampleType.equals("New")) 
				datasetPath = "/home/" + username + "/workspace/Dataset/TSC_Problems/";
			else
				datasetPath = "/home/" + username + "/workspace/Dataset/UCR_Time_Series_Archive/";
		}
		   
		// Get arguments 
		if (args.length >= 1) projectPath = args[0];
		if (args.length >= 2) datasetPath = args[1];
		if (args.length >= 3) sampleType = args[2];
		if (sampleType.equals("Single") && args.length >= 4) {
			datasetName = args[3];
			if (args.length >= 5) nbRuns = Integer.parseInt(args[4]);
		} else if (args.length >= 4) {
			nbRuns = Integer.parseInt(args[3]);
		} 
		
		if (sampleType.equals("Single")) 
			System.out.println("Find best warping window with " + method + " on " + datasetName + " dataset -- " + nbRuns + " runs");
		else
			System.out.println("Find best warping window with " + method + " on " + sampleType + " dataset -- " + nbRuns + " runs");
		
				
		// Run the experiment depending on the given type
		switch(sampleType) {
		case "Sorted":
			for (int j = 0; j < UCRArchive.sortedDataset.length; j++) {
				datasetName = UCRArchive.sortedDataset[j];
				singleProblem(datasetName);
			}
			break;
		case "Small":
			for (int j = 0; j < UCRArchive.smallDataset.length; j++) {
				datasetName = UCRArchive.smallDataset[j];
				singleProblem(datasetName);
			}
			break;
		case "New":
			for (int j = 0; j < UCRArchive.newTSCProblems.length; j++) {
				datasetName = UCRArchive.newTSCProblems[j];
				singleProblem(datasetName);
			}
			break;
		case "All":
			File rep = new File(datasetPath);
	        File[] listData = rep.listFiles(new FileFilter() {
	            @Override
	            public boolean accept(File pathname) {
	                return pathname.isDirectory();
	            }
	        });
	        Arrays.sort(listData);

	        for (File dataRep : listData) {	                        
	            datasetName = dataRep.getName();
	            singleProblem(datasetName);
	        }
	        break;
		case "Single":
			singleProblem(datasetName);
			break;
		}		
    }// End main
	
	/**
	 * Running the experiment for a single dataset
	 * @param datasetName
	 * @throws Exception
	 */
	private static void singleProblem (String datasetName) throws Exception {
		// Setting output directory
		resDir = projectPath + "outputs/Benchmark/" + datasetName + "/";
        
		// Check if it exist, else create the directory
        File dir = new File(resDir);
        if (!dir.exists())
        	dir.mkdirs();
        
        // Reading the dataset
        System.out.println("Processing: " + datasetName);
        Instances[] data = ExperimentsLauncher.readTrainAndTest(datasetPath, datasetName);

        Instances train = data[0];
        Instances test = data[1];
        
     // Go through different runs and randomize the dataset
        for (int i = 0; i < nbRuns; i++) {
        	// Sampling the dataset
        	train = Sampling.random(train);        	
        	
        	// Initialising the classifier
        	System.out.println("Run " + i + ", Launching " + method);
        	UCRSuite classifier = new UCRSuite(datasetName);
        	classifier.setResDir(resDir);
        	classifier.setType(method);
        	
        	// Training the classifier for best window
        	long start = System.nanoTime();
        	classifier.buildClassifier(train);
        	long stop = System.nanoTime();
        	double searchTime = (stop - start)/1e9;
        	System.out.println(searchTime + " s");

        	bestWarpingWindow = classifier.getBestWin();
        	bestScore = classifier.getBestScore();

        	// Evaluate the trained classfier with test set
        	Evaluation eval = new Evaluation(train);
        	eval.evaluateModel(classifier, test);
        	System.out.println(eval.errorRate());

        	// Save result
        	saveSearchTime(searchTime, eval.errorRate());
        }
	}
	
	/**
	 * Save results (search time) to csv
	 * @param searchTime
	 * @param error
	 */
	private static void saveSearchTime(double searchTime, double error) {
		String fileName = resDir + datasetName + "_result_" + method + ".csv";
		FileWriter out;
		boolean append = false;
		File file = new File(fileName);
		if (file.exists()) 
			append = true;
		try {
			out = new FileWriter(fileName, append);
			if (!append)
				out.append("SearchTime(s),BestWin,BestScore,TestError\n");
			out.append(searchTime + "," + bestWarpingWindow + "," + bestScore + "," + error + "\n");
			out.flush();
	        out.close();
		} catch (IOException e) {
			e.printStackTrace();
		}
	}
}
